The human perception isn’t built for observing fine changes in grayscale images. Human eyes are more sensitive to observing changes between colors, so you often need to recolor your grayscale images to get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your computer vision application.

In OpenCV 2.4 you only need applyColorMap() to apply a colormap on a given image. The following sample code reads the path to an image from command line, applies a Jet colormap on it and shows the result:

#include<opencv2/contrib/contrib.hpp>#include<opencv2/core/core.hpp>#include<opencv2/highgui/highgui.hpp>usingnamespacecv;intmain(intargc,constchar*argv[]){// Get the path to the image, if it was given// if no arguments were given.stringfilename;if(argc>1){filename=string(argv[1]);}// The following lines show how to apply a colormap on a given image// and show it with cv::imshow example with an image. An exception is// thrown if the path to the image is invalid.if(!filename.empty()){Matimg0=imread(filename);// Throw an exception, if the image can't be read:if(img0.empty()){CV_Error(CV_StsBadArg,"Sample image is empty. Please adjust your path, so it points to a valid input image!");}// Holds the colormap version of the image:Matcm_img0;// Apply the colormap:applyColorMap(img0,cm_img0,COLORMAP_JET);// Show the result:imshow("cm_img0",cm_img0);waitKey(0);}return0;}